Design, build, and deploy production-grade agentic AI systems.
Duration : 20 Hours
A hands-on engineering program for developers and technical professionals building multi-agent systems with LLMs, tools, memory, and orchestration.
New to Web3
No coding background
Aspiring for Remarkable Growth
Prefers interactive learning
Desires practical experience
Prepare for Success
Audience Fit Check
Who this is for / not for
Fast filter so the right builders join — and the wrong audience self-selects out.
This program is for:
Ideal Fit- Software engineers with Python experience
- Developers building AI-powered products or platforms
- Tech leads and architects exploring agent-based systems
This program is NOT for:
Not a Fit- Beginners with no coding background
- People looking for AI theory or prompt-only workflows
- Non-technical or no-code audiences
If you match the “For” list, you’ll build real systems — not just concepts.
The focus is on system design, trade-offs, and real-world constraints, not toy examples.
What You Will Build
Concrete deliverables you’ll finish during the program (not just learn about).
A multi-agent AI system with task delegation and coordination
An agent using tools, memory, and external data sources (RAG)
An end-to-end agentic workflow deployed as an application or service
A capstone project that can be extended to real production use
Program Eligibility
Architectural decisions are discussed in terms of scalability, latency, cost, and reliability.
Prerequisite: Intermediate Python knowledge.
Build Autonomous AI Workers (Python & LangChain)
27K+ Students Enrolled
AED 4999
+Taxes
Course Schedule
Modules include common failure modes, debugging strategies, and design trade-offs seen in real agentic systems.
- LLM Fundamentals: Core concepts of transformers, model training (fine-tuning vs. RAG), and optimizing models for latency and cost (e.g., using open-source models like Llama, Groq, or DeepSeek).
- The Agent Architecture: Deep dive into the core components: Planning, Memory (short-term & long-term), Tool-Use, and Reflection/Self-Correction.
- Prompt Engineering for Agents: Mastering advanced techniques like Chain-of-Thought (CoT), Tree-of-Thought (ToT), and using Pydantic for reliable, structured output from LLMs.
- Mastering CrewAI/LangGraph: Hands-on development of specialized multi-agent systems using the industry’s leading frameworks.
- Role-Based Collaboration: Defining roles, goals, and processes for collaborative agents (e.g., a Researcher Agent feeding information to a Writer Agent).
- Tool Creation & Function Calling: Equipping agents with custom tools (API access, database queries, web scraping) and implementing complex, multi-step function calling for real-world tasks.
- Advanced Flow Engineering: Designing dynamic, event-driven workflows using CrewAI Flows and LangGraph state machines for precise control and auditing of complex tasks.
- RAG Architecture Deep Dive: Moving beyond Naive RAG to implement advanced techniques like Query Transformation (e.g., Hypothetical Document Embeddings) and Reranking for high-quality context retrieval.
- Vector Database Implementation: Practical labs using production-grade vector databases (e.g., Pinecone, Qdrant, or Chroma) for high-speed, semantic search.
- Data Preparation: Mastering text chunking strategies, metadata filtering, and embedding model selection for optimal retrieval performance.
- Agentic RAG: Integrating RAG directly into the agent’s memory and reasoning process, allowing agents to autonomously decide when and how to retrieve external information.
- Containerization & Scaling: Packaging agents using Docker and deploying them reliably using Kubernetes or serverless functions for horizontal scaling.
- CI/CD for AI Agents: Setting up automated testing and deployment pipelines to manage agent versions and dependencies efficiently.
- Observability & Monitoring: Implementing logging, metrics, and tracing (LangSmith or equivalent) to monitor agent cost, latency, and model drift in production.
- Security & Compliance: Best practices for securing LLM API keys, managing user data, and auditing agent behavior for enterprise compliance and ethics.
Additional Activities
You will deploy agentic systems that can run as APIs, services, or internal tools—not just notebooks or demos.
- The Promise: Stop writing code; start building autonomous workers. You will jump ahead of 90% of developers who are still focused only on basic LLM calls.
- Webinars and Workshops: Interactive sessions with industry experts.
- Networking Events: Opportunities to build connections with peers and professionals.
- Support Sessions: Guidance and assistance for queries and additional learning.
- Final Presentation: Present your capstone project to a panel of experts.
Beyond Academics
Additional Activities Throughout the Program
Participants graduate with a certificate and a production-ready project demonstrating agentic system design.
Upon completing the course, you will receive a certificate—an impactful addition to your LinkedIn profile that can capture the interest of our hiring partners and prominent big data companies.